Abstract
The symbol control task tests how processing of visually presented symbols changes after they become associated with speech sounds. There are two test times before and after an audiovisual feedback learning task. The type of items presented are 1. letters (overlearned, associated with speech sounds, visually familiar ), and 2. ‘learned falsefonts’ ( part of the feedback learning task) + two control stimuli: 3. ‘familiar’ false fonts (repeated before and after in the experiment) and 4.new false font (new in each two test times).The current study is based on a sample 39 healthy adult participants (X females; age XX ± XX, range X-X , XX). The current study sample was obtained from an initial pool of 44 participants from which 3 participants were excluded due to excessive motion in the scanner and 1 was excluded due to technical problems during scanning.
MRI data were recorded on a Philips Achieva 3 Tesla scanner (Best, The Netherlands) using a 32-element receive head coil. Using a T2-weighted whole-brain gradient-echo planar image sequence, 440 volumes were acquired for each experimental block [Slices = XXX; repetition time = XX s; echo time = XXX ms; slice gap = XXX mm; voxel size = 3 x 3 x 3.5 mm3; flip angle = XXX°; field of view = 240 x 127.5 x 240 mm2; SENSE-factor = 2]. In addition, a field map and a high-resolution T1-weighted anatomical image were acquired. fMRI data preprocessing and analysis were performed in the SPM12 toolbox. Preprocessing included b0 fieldmap correction, slice time correction and corregistration of the functional data to the T1-weighted image. The deformation fields derived from segmentation of the T1 image were used for normalization to the Montreal Neurological Institute (MNI) -152 template space. Last, smoothing with a 6 mm full-width-half-maximum kernel was applied to the functional data.
Motion artifacts were assessed by calculating the framewise displacement (FD) values of each subject and task recording. Only subjects with FD < 0.5 mm were included in analysis (mean XX±XX; no excluded participants based on this criteria). Moreover, single volumes with FD > 1 were censored in the statistical analyses using an additional binary regressor (mean XX±XX % of volumes excluded; and a maximum of XX % excluded in one participant).
# Data read, trim, reshape
df <- readxl::read_xlsx('O:/studies/grapholemo/LEMO_Master.xlsx',sheet = 'MRI')
df <- dplyr::select(df,c(subjID,grep('FWD_*symCtrl*',colnames(df)) ))
colnames(df) <-gsub('FWD_','',colnames(df))
dflong <- tidyr::pivot_longer(df,colnames(df)[-1]) %>% separate(name,c('task','measure'),sep="__")
dflong$measure <- as.factor(dflong$measure)
levels(dflong$measure) <- c('max','mean','n volumes with FD > 1')
#Exclude subjects rows with no MRI data
dflong<-dflong[!is.na(dflong$value),]
#exclude subjects based on mean FD
`%nin%` = Negate(`%in%`)
excludedSubjects <-dflong$subjID[which(dflong$value > 0.5 & dflong$measure == 'mean')]
dflong<-dflong[which(dflong$subjID %nin% excludedSubjects),]
#exclude subjects based on % of 'bad scans'
#excludedSubjects2 <-dflong$subjID[which(dflong$ > 0.5 & dflong$measure == 'mean')]
#Plot
ggplot(dflong,aes(x=task,y=value,fill=measure))+
geom_point(shape=21,alpha=.5,color='black',size=2)+
facet_wrap(~measure,scales='free')+
theme_bw() +
scale_fill_discrete(guide="none")+
scale_x_discrete(limits = rev)+
labs(title=paste0('Framewise displacement (N=',length(unique(dflong$subjID)),')'),
subtitle = paste0(length(excludedSubjects), ' subject(s) was excluded based on mean FD'))
dat <- readxl::read_xlsx('O:/studies/grapholemo/analysis/LEMO_GFG/mri/2ndLevel/SymbolControl/symCtrl_pre/Result_regions_2Lv_GLM0.xlsx')
#some formatting
dat$file <- substr(dat$file,unlist(gregexpr('con_0',dat$file)),unlist(gregexpr('con_0',dat$file))+7) #keep just the con_**** as name
dat <- dplyr::relocate(dat,'aal',2)
dat <- dplyr::relocate(dat,'file',1)
cons2plot <- unique(dat$file)
# Rename contrasts
urfile <- dat$file
renamecontrasts <- function(originalNames){
originalNames <- gsub('con_0001','L>FFL',originalNames)
originalNames <- gsub('con_0002','L>FFF',originalNames)
originalNames <- gsub('con_0003','L>FFN',originalNames)
originalNames <- gsub('con_0004','FFL>L',originalNames)
originalNames <- gsub('con_0005','FFL>FFF',originalNames)
originalNames <- gsub('con_0006','FFL>FFN',originalNames)
originalNames <- gsub('con_0007','FFF>L',originalNames)
originalNames <- gsub('con_0008','FFN>L',originalNames)
originalNames <- gsub('con_0009','L>.',originalNames)
originalNames <- gsub('con_0010','FFL>.',originalNames)
originalNames <- gsub('con_0011','FFF>.',originalNames)
originalNames <- gsub('con_0012','FFN>.',originalNames)
}
dat$file <- renamecontrasts(dat$file)
knitr::kable(dat,caption='', digits=3) %>%
#kable_styling(bootstrap_options = c("striped", "hover"), full_width = F) %>%
column_spec(1, bold = T, border_right = T ) %>%
kable_classic(full_width = F, html_font = "Calibri")
| file | aal | xcoord | ycoord | zcoord | cluster_pFWE | cluster_punc | cluster_k | peak_Z | peak_T | peak_pFWE | peak_punc | T_heightThresh | dist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| L>FFL | Cuneus_L | -8 | -93 | 18 | 0.000 | 0 | 171 | 4.88 | 5.81 | 0.040 | 0 | 3.319 | 0 |
| L>FFL | Temporal_Mid_L | -50 | -48 | 15 | 0.000 | 0 | 377 | 4.31 | 4.93 | 0.327 | 0 | 3.319 | 0 |
| L>FFL | Temporal_Mid_R | 70 | -54 | 9 | 0.000 | 0 | 247 | 4.05 | 4.56 | 0.632 | 0 | 3.319 | 4 |
| L>FFF | Temporal_Mid_L | -53 | -45 | -6 | 0.000 | 0 | 164 | 4.1 | 4.63 | 0.541 | 0 | 3.327 | 0 |
| L>FFN | Occipital_Inf_L | -17 | -93 | -9 | 0.000 | 0 | 192 | 5.62 | 7.08 | 0.001 | 0 | 3.324 | 0 |
| L>FFN | Cuneus_R | 19 | -96 | 9 | 0.000 | 0 | 201 | 5.15 | 6.25 | 0.010 | 0 | 3.324 | 0 |
| L>FFN | Precentral_L | -62 | 12 | 33 | 0.006 | 4e-04 | 89 | 4 | 4.50 | 0.689 | 0 | 3.324 | 1 |
| FFL>L | Occipital_Mid_L | -35 | -93 | 3 | 0.000 | 0 | 844 | 6.31 | 8.46 | 0.000 | 0 | 3.443 | 0 |
| FFL>L | Occipital_Mid_R | 37 | -87 | 15 | 0.000 | 0 | 914 | 6.11 | 8.04 | 0.000 | 0 | 3.443 | 0 |
| FFL>FFF | Lingual_L | -20 | -84 | -12 | 0.001 | 1e-04 | 145 | 5.95 | 7.73 | 0.000 | 0 | 3.38 | 0 |
| FFL>FFF | Calcarine_R | 19 | -96 | -3 | 0.005 | 4e-04 | 105 | 4.49 | 5.20 | 0.159 | 0 | 3.38 | 0 |
| FFL>FFN | Calcarine_R | 22 | -93 | -3 | 0.000 | 0 | 622 | 7.48 | 11.43 | 0.000 | 0 | 3.35 | 0 |
| FFL>FFN | Occipital_Mid_L | -23 | -96 | 6 | 0.000 | 0 | 700 | 6.92 | 9.90 | 0.000 | 0 | 3.35 | 0 |
| FFF>L | Occipital_Mid_R | 34 | -81 | 3 | 0.000 | 0 | 929 | 6.43 | 8.73 | 0.000 | 0 | 3.482 | 0 |
| FFF>L | Occipital_Mid_L | -44 | -87 | 3 | 0.000 | 0 | 549 | 5.93 | 7.67 | 0.000 | 0 | 3.482 | 0 |
| FFN>L | Occipital_Mid_L | -47 | -78 | 0 | 0.000 | 0 | 363 | 6.69 | 9.34 | 0.000 | 0 | 3.388 | 0 |
| FFN>L | Occipital_Inf_R | 43 | -75 | 0 | 0.000 | 0 | 656 | 6 | 7.81 | 0.000 | 0 | 3.388 | 0 |
| L>. | Occipital_Inf_R | 34 | -90 | 0 | 0.000 | 0 | 4479 | Inf | 23.42 | 0.000 | 0 | 3.325 | 0 |
| L>. | Parietal_Inf_R | 28 | -54 | 51 | 0.000 | 0 | 284 | 6.11 | 8.04 | 0.000 | 0 | 3.325 | 0 |
| L>. | Precentral_R | 55 | 3 | 51 | 0.000 | 0 | 351 | 5.65 | 7.14 | 0.001 | 0 | 3.325 | 0 |
| L>. | Parietal_Inf_L | -29 | -51 | 48 | 0.000 | 0 | 289 | 5.59 | 7.03 | 0.001 | 0 | 3.325 | 0 |
| L>. | Precentral_L | -47 | 3 | 39 | 0.000 | 0 | 483 | 5.53 | 6.92 | 0.001 | 0 | 3.325 | 0 |
| L>. | Supp_Motor_Area_R | 4 | 6 | 60 | 0.000 | 0 | 392 | 5.43 | 6.73 | 0.002 | 0 | 3.325 | 0 |
| L>. | Frontal_Mid_L | -41 | 33 | 45 | 0.008 | 5e-04 | 80 | 4.33 | 4.96 | 0.331 | 0 | 3.325 | 0 |
| L>. | Frontal_Sup_Medial_R | 7 | 60 | 42 | 0.039 | 0.0024 | 56 | 4.29 | 4.90 | 0.371 | 0 | 3.325 | 1 |
| L>. | Frontal_Inf_Orb_L | -41 | 48 | -15 | 0.011 | 6e-04 | 75 | 4.23 | 4.82 | 0.438 | 0 | 3.325 | 0 |
| FFL>. | Occipital_Inf_R | 34 | -90 | 0 | 0.000 | 0 | 2173 | Inf | 20.44 | 0.000 | 0 | 3.325 | 0 |
| FFL>. | Occipital_Inf_L | -38 | -84 | -9 | 0.000 | 0 | 2040 | Inf | 19.38 | 0.000 | 0 | 3.325 | 0 |
| FFL>. | Precentral_R | 55 | 3 | 51 | 0.000 | 0 | 179 | 5.77 | 7.37 | 0.000 | 0 | 3.325 | 0 |
| FFL>. | Caudate_L | -5 | 21 | 6 | 0.003 | 1e-04 | 88 | 5.54 | 6.93 | 0.001 | 0 | 3.325 | 1.41 |
| FFL>. | Angular_R | 28 | -57 | 54 | 0.001 | 0 | 106 | 5.24 | 6.41 | 0.006 | 0 | 3.325 | 0 |
| FFL>. | Precentral_L | -56 | -3 | 48 | 0.000 | 0 | 293 | 5.2 | 6.33 | 0.008 | 0 | 3.325 | 0 |
| FFL>. | Hippocampus_L | -17 | -33 | 0 | 0.010 | 6e-04 | 69 | 5.12 | 6.20 | 0.012 | 0 | 3.325 | 0 |
| FFL>. | Parietal_Inf_L | -29 | -51 | 48 | 0.000 | 0 | 127 | 4.74 | 5.58 | 0.082 | 0 | 3.325 | 0 |
| FFL>. | Occipital_Mid_R | 28 | -69 | 30 | 0.022 | 0.0012 | 59 | 4.73 | 5.56 | 0.088 | 0 | 3.325 | 0 |
| FFL>. | Thalamus_L | -14 | -30 | 24 | 0.000 | 0 | 178 | 4.65 | 5.44 | 0.122 | 0 | 3.325 | 7.81 |
| FFL>. | Supp_Motor_Area_L | -8 | 9 | 51 | 0.001 | 0 | 109 | 4.51 | 5.23 | 0.202 | 0 | 3.325 | 0 |
| FFL>. | Frontal_Inf_Tri_L | -56 | 33 | 21 | 0.011 | 6e-04 | 68 | 4.22 | 4.80 | 0.491 | 0 | 3.325 | 0 |
| FFF>. | Occipital_Inf_R | 34 | -90 | 0 | 0.000 | 0 | 2204 | Inf | 21.32 | 0.000 | 0 | 3.319 | 0 |
| FFF>. | Occipital_Mid_L | -29 | -93 | 0 | 0.000 | 0 | 2017 | Inf | 18.45 | 0.000 | 0 | 3.319 | 0 |
| FFF>. | Parietal_Sup_R | 28 | -57 | 57 | 0.000 | 0 | 187 | 5.59 | 7.03 | 0.001 | 0 | 3.319 | 0 |
| FFF>. | Parietal_Inf_L | -29 | -45 | 45 | 0.000 | 0 | 180 | 5.5 | 6.86 | 0.002 | 0 | 3.319 | 0 |
| FFF>. | Precentral_R | 55 | 3 | 48 | 0.003 | 2e-04 | 109 | 5.27 | 6.45 | 0.005 | 0 | 3.319 | 0 |
| FFF>. | Frontal_Mid_L | -44 | 27 | 48 | 0.014 | 0.001 | 79 | 5.23 | 6.38 | 0.007 | 0 | 3.319 | 0 |
| FFF>. | Caudate_L | -14 | 24 | 9 | 0.000 | 0 | 178 | 5.07 | 6.12 | 0.015 | 0 | 3.319 | 0 |
| FFF>. | Precentral_L | -56 | -3 | 51 | 0.000 | 0 | 181 | 5.07 | 6.12 | 0.015 | 0 | 3.319 | 1 |
| FFF>. | Occipital_Mid_R | 28 | -75 | 33 | 0.033 | 0.0023 | 65 | 4.89 | 5.82 | 0.037 | 0 | 3.319 | 0 |
| FFF>. | Hippocampus_L | -26 | -39 | 15 | 0.031 | 0.0021 | 66 | 4.67 | 5.47 | 0.090 | 0 | 3.319 | 5.48 |
| FFF>. | Frontal_Mid_Orb_R | 37 | 66 | -9 | 0.015 | 0.001 | 78 | 4.22 | 4.80 | 0.408 | 0 | 3.319 | 2 |
| FFN>. | Fusiform_R | 37 | -66 | -15 | 0.000 | 0 | 2145 | Inf | 19.61 | 0.000 | 0 | 3.322 | 0 |
| FFN>. | Occipital_Inf_L | -35 | -84 | -6 | 0.000 | 0 | 2175 | Inf | 17.68 | 0.000 | 0 | 3.322 | 0 |
| FFN>. | Angular_R | 28 | -57 | 54 | 0.000 | 0 | 375 | 6.09 | 8.00 | 0.000 | 0 | 3.322 | 0 |
| FFN>. | Precentral_R | 52 | 3 | 51 | 0.000 | 0 | 244 | 5.92 | 7.67 | 0.000 | 0 | 3.322 | 0 |
| FFN>. | Postcentral_L | -56 | -6 | 51 | 0.000 | 0 | 299 | 5.64 | 7.11 | 0.001 | 0 | 3.322 | 1 |
| FFN>. | Cingulum_Post_R | 13 | -36 | 21 | 0.010 | 6e-04 | 74 | 4.92 | 5.86 | 0.034 | 0 | 3.322 | 4.24 |
| FFN>. | Supp_Motor_Area_L | -5 | -3 | 63 | 0.000 | 0 | 133 | 4.61 | 5.38 | 0.133 | 0 | 3.322 | 0 |
| FFN>. | Precuneus_L | -23 | -42 | 21 | 0.008 | 4e-04 | 78 | 4.59 | 5.35 | 0.142 | 0 | 3.322 | 9.9 |
dat <- readxl::read_xlsx('O:/studies/grapholemo/analysis/LEMO_GFG/mri/2ndLevel/SymbolControl/symCtrl_post/Result_regions_2Lv_GLM0.xlsx')
#some formatting
dat$file <- substr(dat$file,11,18)
dat <- relocate(dat,'aal',2)
dat <- relocate(dat,'file',1)
cons2plot <- unique(dat$file)
# Rename contrasts
urfile <- dat$file
dat$file <- renamecontrasts(dat$file)
knitr::kable(dat,caption='', digits=3) %>%
#kable_styling(bootstrap_options = c("striped", "hover"), full_width = F) %>%
column_spec(1, bold = T, border_right = T ) %>%
kable_classic(full_width = F, html_font = "Calibri")
| file | aal | xcoord | ycoord | zcoord | cluster_pFWE | cluster_punc | cluster_k | peak_Z | peak_T | peak_pFWE | peak_punc | T_heightThresh | dist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| L>FFL | Cuneus_R | 10 | -81 | 27 | 0.000 | 0 | 220 | 4.3 | 4.91 | 0.316 | 0 | 3.324 | 0 |
| L>FFL | Precuneus_R | 10 | -45 | 54 | 0.002 | 1e-04 | 123 | 4.2 | 4.77 | 0.420 | 0 | 3.324 | 0 |
| L>FFL | SupraMarginal_L | -56 | -27 | 21 | 0.000 | 0 | 221 | 4.17 | 4.73 | 0.452 | 0 | 3.324 | 0 |
| L>FFL | SupraMarginal_R | 58 | -24 | 27 | 0.002 | 1e-04 | 121 | 4.15 | 4.71 | 0.470 | 0 | 3.324 | 0 |
| L>FFL | Temporal_Mid_R | 49 | -60 | 12 | 0.000 | 0 | 181 | 4.15 | 4.70 | 0.476 | 0 | 3.324 | 0 |
| L>FFL | Rolandic_Oper_R | 55 | 6 | 6 | 0.022 | 0.0016 | 73 | 4 | 4.49 | 0.658 | 0 | 3.324 | 0 |
| L>FFF | Precentral_L | -53 | 6 | 36 | 0.000 | 0 | 216 | 4.04 | 4.54 | 0.597 | 0 | 3.42 | 0 |
| L>FFN | Calcarine_R | 16 | -96 | 6 | 0.018 | 0.0013 | 76 | 4.83 | 5.73 | 0.045 | 0 | 3.343 | 0 |
| FFL>L | Occipital_Mid_R | 34 | -90 | 9 | 0.000 | 0 | 908 | 6.74 | 9.46 | 0.000 | 0 | 3.337 | 0 |
| FFL>L | Occipital_Mid_L | -32 | -87 | 0 | 0.000 | 0 | 896 | 6.11 | 8.03 | 0.000 | 0 | 3.337 | 0 |
| FFL>L | Cingulum_Mid_R | 10 | -27 | 27 | 0.000 | 0 | 175 | 4.78 | 5.65 | 0.055 | 0 | 3.337 | 4.9 |
| FFL>L | Occipital_Sup_R | 28 | -63 | 33 | 0.000 | 0 | 215 | 4.74 | 5.59 | 0.064 | 0 | 3.337 | 0 |
| FFL>FFF | Occipital_Inf_R | 22 | -90 | -3 | 0.000 | 0 | 246 | 7.04 | 10.22 | 0.000 | 0 | 3.337 | 0 |
| FFL>FFF | Calcarine_L | -11 | -93 | -12 | 0.000 | 0 | 297 | 5.62 | 7.08 | 0.001 | 0 | 3.337 | 0 |
| FFL>FFF | Occipital_Sup_R | 22 | -63 | 45 | 0.000 | 0 | 1227 | 5.08 | 6.13 | 0.014 | 0 | 3.337 | 0 |
| FFL>FFF | Frontal_Inf_Oper_R | 40 | 12 | 27 | 0.000 | 0 | 170 | 4.79 | 5.66 | 0.051 | 0 | 3.337 | 0 |
| FFL>FFF | Insula_L | -29 | 27 | -3 | 0.004 | 3e-04 | 108 | 4.72 | 5.55 | 0.067 | 0 | 3.337 | 0 |
| FFL>FFF | Cingulum_Post_L | 1 | -33 | 27 | 0.002 | 1e-04 | 126 | 4.63 | 5.41 | 0.095 | 0 | 3.337 | 2.24 |
| FFL>FFF | Frontal_Inf_Oper_L | -44 | 12 | 21 | 0.000 | 0 | 273 | 4.51 | 5.22 | 0.151 | 0 | 3.337 | 0 |
| FFL>FFF | Supp_Motor_Area_L | -2 | 18 | 45 | 0.001 | 1e-04 | 142 | 4.2 | 4.78 | 0.395 | 0 | 3.337 | 0 |
| FFL>FFN | Occipital_Inf_L | -17 | -93 | -9 | 0.000 | 0 | 451 | 6.67 | 9.29 | 0.000 | 0 | 3.323 | 0 |
| FFL>FFN | Calcarine_R | 19 | -96 | 0 | 0.000 | 0 | 329 | 6.32 | 8.50 | 0.000 | 0 | 3.323 | 0 |
| FFL>FFN | Parietal_Inf_L | -32 | -57 | 45 | 0.000 | 0 | 390 | 4.56 | 5.30 | 0.137 | 0 | 3.323 | 0 |
| FFL>FFN | Precuneus_L | -5 | -63 | 42 | 0.000 | 0 | 524 | 4.52 | 5.25 | 0.155 | 0 | 3.323 | 0 |
| FFL>FFN | Cingulum_Post_L | 1 | -36 | 27 | 0.000 | 0 | 181 | 4.46 | 5.15 | 0.195 | 0 | 3.323 | 1.41 |
| FFL>FFN | Frontal_Mid_L | -50 | 24 | 42 | 0.000 | 0 | 249 | 4.37 | 5.01 | 0.264 | 0 | 3.323 | 0 |
| FFL>FFN | Cingulum_Mid_R | 1 | 21 | 42 | 0.001 | 0 | 137 | 4.22 | 4.80 | 0.408 | 0 | 3.323 | 0 |
| FFL>FFN | Frontal_Mid_L | -38 | 57 | 12 | 0.048 | 0.0033 | 58 | 4.06 | 4.58 | 0.593 | 0 | 3.323 | 0 |
| FFF>L | Occipital_Mid_L | -50 | -84 | 6 | 0.000 | 0 | 585 | 6.41 | 8.70 | 0.000 | 0 | 3.323 | 2.83 |
| FFF>L | Occipital_Mid_R | 40 | -90 | 3 | 0.000 | 0 | 718 | 6.15 | 8.13 | 0.000 | 0 | 3.323 | 0 |
| FFN>L | Occipital_Mid_R | 34 | -81 | 6 | 0.000 | 0 | 1410 | 6.84 | 9.70 | 0.000 | 0 | 3.328 | 0 |
| FFN>L | Occipital_Mid_L | -50 | -84 | 3 | 0.000 | 0 | 906 | 6.08 | 7.99 | 0.000 | 0 | 3.328 | 2.83 |
| L>. | Occipital_Mid_R | 34 | -93 | 3 | 0.000 | 0 | 1707 | Inf | 15.78 | 0.000 | 0 | 3.336 | 0 |
| L>. | Occipital_Inf_L | -35 | -84 | -6 | 0.000 | 0 | 1513 | Inf | 15.11 | 0.000 | 0 | 3.336 | 0 |
| L>. | Precentral_L | -47 | 0 | 54 | 0.000 | 0 | 183 | 4.58 | 5.33 | 0.110 | 0 | 3.336 | 0 |
| L>. | Parietal_Sup_L | -32 | -63 | 57 | 0.000 | 0 | 214 | 4.58 | 5.33 | 0.111 | 0 | 3.336 | 0 |
| L>. | Supp_Motor_Area_L | -5 | 6 | 54 | 0.016 | 0.0013 | 85 | 4.33 | 4.96 | 0.264 | 0 | 3.336 | 0 |
| FFL>. | Occipital_Inf_L | -35 | -87 | -9 | 0.000 | 0 | 1776 | Inf | 17.81 | 0.000 | 0 | 3.328 | 0 |
| FFL>. | Occipital_Mid_R | 31 | -93 | 3 | 0.000 | 0 | 2010 | Inf | 17.08 | 0.000 | 0 | 3.328 | 0 |
| FFL>. | Parietal_Sup_L | -32 | -63 | 60 | 0.000 | 0 | 686 | 5.8 | 7.43 | 0.000 | 0 | 3.328 | 0 |
| FFL>. | Precentral_L | -47 | 3 | 54 | 0.000 | 0 | 663 | 5.56 | 6.97 | 0.001 | 0 | 3.328 | 0 |
| FFL>. | Angular_R | 31 | -57 | 51 | 0.000 | 0 | 366 | 5.3 | 6.51 | 0.004 | 0 | 3.328 | 0 |
| FFL>. | Frontal_Mid_R | 49 | 21 | 51 | 0.000 | 0 | 338 | 4.89 | 5.82 | 0.037 | 0 | 3.328 | 0 |
| FFL>. | Supp_Motor_Area_L | -8 | 12 | 45 | 0.005 | 3e-04 | 96 | 4.26 | 4.87 | 0.358 | 0 | 3.328 | 0 |
| FFL>. | Cingulum_Mid_L | -14 | -33 | 27 | 0.017 | 0.0011 | 75 | 4.15 | 4.71 | 0.482 | 0 | 3.328 | 6.16 |
| FFF>. | Occipital_Inf_R | 34 | -90 | 0 | 0.000 | 0 | 1883 | Inf | 18.38 | 0.000 | 0 | 3.32 | 0 |
| FFF>. | Occipital_Mid_L | -29 | -93 | 3 | 0.000 | 0 | 1608 | Inf | 16.62 | 0.000 | 0 | 3.32 | 0 |
| FFN>. | Occipital_Inf_L | -35 | -84 | -6 | 0.000 | 0 | 1750 | Inf | 17.07 | 0.000 | 0 | 3.341 | 0 |
| FFN>. | Temporal_Inf_R | 49 | -69 | -9 | 0.000 | 0 | 2069 | Inf | 16.44 | 0.000 | 0 | 3.341 | 0 |
| FFN>. | Precentral_L | -50 | 0 | 54 | 0.000 | 0 | 169 | 6.53 | 8.97 | 0.000 | 0 | 3.341 | 0 |
| FFN>. | Precentral_R | 55 | 6 | 48 | 0.000 | 0 | 179 | 5.65 | 7.13 | 0.001 | 0 | 3.341 | 0 |
| FFN>. | Angular_R | 28 | -57 | 54 | 0.006 | 4e-04 | 83 | 5.16 | 6.26 | 0.010 | 0 | 3.341 | 0 |
| FFN>. | Occipital_Mid_R | 31 | -69 | 27 | 0.043 | 0.0026 | 54 | 4.98 | 5.96 | 0.025 | 0 | 3.341 | 0 |
| FFN>. | Parietal_Sup_L | -29 | -63 | 57 | 0.001 | 1e-04 | 116 | 4.59 | 5.36 | 0.134 | 0 | 3.341 | 0 |
dat <- readxl::read_xlsx('O:/studies/grapholemo/analysis/LEMO_GFG/mri/2ndLevel_pairedTs/symCtrl_prePost/Result_regions_2Lv_GLM0prePost.xlsx')
#some formatting
dat$file <- substr(dat$file,11,18)
dat <- relocate(dat,'aal',2)
dat <- relocate(dat,'file',1)
cons2plot <- unique(dat$file)
# Rename contrasts
urfile <- dat$file
dat$file <- renamecontrasts(dat$file)
knitr::kable(dat,caption='', digits=3) %>%
#kable_styling(bootstrap_options = c("striped", "hover"), full_width = F) %>%
column_spec(1, bold = T, border_right = T ) %>%
kable_classic(full_width = F, html_font = "Calibri")
| file | aal | xcoord | ycoord | zcoord | cluster_pFWE | cluster_punc | cluster_k | peak_Z | peak_T | peak_pFWE | peak_punc | T_heightThresh | dist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| L>FFL | Occipital_Mid_R | 31 | -63 | 33 | 0.000 | 0 | 274 | 4.75 | 5.60 | 0.062 | 0 | 3.32 | 0 |
| FFF>. | Calcarine_R | 7 | -78 | 6 | 0.000 | 0 | 301 | 4.27 | 4.87 | 0.377 | 0 | 3.327 | 0 |
| FFF>. | Hippocampus_L | -26 | -39 | 18 | 0.002 | 1e-04 | 109 | 4.06 | 4.58 | 0.621 | 0 | 3.327 | 8.31 |
dat <- readxl::read_xlsx('O:/studies/grapholemo/analysis/LEMO_GFG/mri/2ndLevel_pairedTs/symCtrl_prePost/Result_regions_2Lv_GLM0postPre.xlsx')
#some formatting
dat$file <- substr(dat$file,11,18)
dat <- relocate(dat,'aal',2)
dat <- relocate(dat,'file',1)
cons2plot <- unique(dat$file)
# Rename contrasts
urfile <- dat$file
dat$file <- renamecontrasts(dat$file)
knitr::kable(dat,caption='', digits=3) %>%
#kable_styling(bootstrap_options = c("striped", "hover"), full_width = F) %>%
column_spec(1, bold = T, border_right = T ) %>%
kable_classic(full_width = F, html_font = "Calibri")
| file | aal | xcoord | ycoord | zcoord | cluster_pFWE | cluster_punc | cluster_k | peak_Z | peak_T | peak_pFWE | peak_punc | T_heightThresh | dist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| FFL>L | Occipital_Mid_R | 31 | -63 | 33 | 0.000 | 0 | 274 | 4.75 | 5.60 | 0.062 | 0 | 3.32 | 0 |
| FFL>FFF | Occipital_Sup_R | 25 | -66 | 45 | 0.000 | 0 | 199 | 4.14 | 4.69 | 0.475 | 0 | 3.341 | 0 |
| FFL>FFF | Parietal_Inf_L | -29 | -51 | 42 | 0.000 | 0 | 333 | 4.13 | 4.67 | 0.489 | 0 | 3.341 | 0 |
| FFL>FFN | Parietal_Inf_L | -29 | -54 | 42 | 0.000 | 0 | 166 | 4.34 | 4.98 | 0.266 | 0 | 3.324 | 0 |
| FFL>FFN | Angular_R | 28 | -54 | 45 | 0.000 | 0 | 189 | 4.21 | 4.79 | 0.393 | 0 | 3.324 | 0 |
| FFL>. | Parietal_Inf_L | -47 | -51 | 45 | 0.003 | 2e-04 | 104 | 3.75 | 4.16 | 0.916 | 1e-04 | 3.334 | 0 |
| FFN>. | Occipital_Inf_R | 37 | -96 | -6 | 0.000 | 0 | 155 | 4.66 | 5.46 | 0.097 | 0 | 3.33 | 1 |
| FFN>. | Occipital_Mid_L | -29 | -102 | -6 | 0.005 | 3e-04 | 91 | 4.39 | 5.05 | 0.256 | 0 | 3.33 | 3.74 |